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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Ulul Albab: Jurnal Studi Islam MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Prosiding Seminas Competitive Advantage Prosiding SNATIKA Vol 01 (2011) Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Knowledge Engineering and Data Science Applied Information System and Management Letters in Information Technology Education (LITE) Journal of Applied Data Sciences Science in Information Technology Letters International Journal of Engineering, Science and Information Technology Bulletin of Social Informatics Theory and Application Jurnal ilmiah teknologi informasi Asia
Fachrul Kurniawan
Universitas Islam Negeri Malang
Religion Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Journal : knowledge engineering and data science

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Market Basket Analysis to Identify Customer Behaviorsby Way of Transaction Data Kurniawan, Fachrul; Umayah, Binti; Hammad, Jihad; Nugroho, Supeno Mardi Susiki; Hariadi, Mochammad
Knowledge Engineering and Data Science
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Abstract

Transaction data is a set of recording data result in connections with sales-purchase activities at a particular company. In these recent years, transaction data have been prevalently used as research objects in means of discovering new information. One of the possible attempts is to design an application that can be used to analyze the existing transaction data. That application has the quality of market basket analysis. In addition, the application is designed to be desktop-based whose components are able to process as well as re-log the existing transaction data. The used method in designing this application is by way of following the existing steps on data mining technique.The trial result showed that the development and the implementation of market basket analysis application through association rule method using apriori algorithm could work well. With the means of confidence value of 46.69% and support value of 1.78%, and the amount of the generated rule was 30 rules.
Indonesian Sentence Boundary Detection using Deep Learning Approaches Santoso, Joan; Setiawan, Esther Irawati; Purwanto, Christian Nathaniel; Kurniawan, Fachrul
Knowledge Engineering and Data Science
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Detecting the sentence boundary is one of the crucial pre-processing steps in natural language processing. It can define the boundary of a sentence since the border between a sentence, and another sentence might be ambiguous. Because there are multiple separators and dynamic sentence patterns, using a full stop at the end of a sentence is sometimes inappropriate. This research uses a deep learning approach to split each sentence from an Indonesian news document. Hence, there is no need to define any handcrafted features or rules. In Part of Speech Tagging and Named Entity Recognition, we use sequence labeling to determine sentence boundaries. Two labels will be used, namely O as a non-boundary token and E as the last token marker in the sentence. To do this, we used the Bi-LSTM approach, which has been widely used in sequence labeling. We have proved that our approach works for Indonesian text using pre-trained embedding in Indonesian, as in previous studies. This study achieved an F1-Score value of 98.49 percent. When compared to previous studies, the achieved performance represents a significant increase in outcomes.
CNN based Face Recognition System for Patients with Down and William Syndrome Setyati, Endang; Az, Suharyono; Hudiono, Subroto Prasetya; Kurniawan, Fachrul
Knowledge Engineering and Data Science
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Down syndrome, also known as trisomy genetic condition, is a genetic disorder that affects many people. Williams syndrome is a hereditary disorder that can affect anyone at birth. It marks medical and cognitive issues, such as cardiovascular illness, developmental delays, and learning impairments. This is accompanied by exceptional verbal abilities, a gregarious attitude, and a passion for music. Down syndrome and William Syndrome are both genetic illnesses. However, it can be distinguished from the arrangement of chromosome 21. Down syndrome and William syndrome can also be identified by recognizing faces, or facial characteristics, such as observing particular facial features. Therefore, this research develops Convolutional Neural Network (CNN) architectures to recognize Down syndrome and William syndrome using a facial recognition approach. A total of 480 facial photos were used in the study, with 390 images used for training data and 90 images used for testing data. The identification class is divided into three categories, Down syndrome, William syndrome, and normal. There are 160 photos in each patient class. This research presents two CNN architectures using a grayscale image of 256×256 pixels. The first CNN architecture comprises 12 layers, while the second comprises 15 layers. The average accuracy results with 12 layers were 91% by attempting to train and test six times. With 15 layers, the average accuracy value is 89%. In comparison, the first architecture has the highest accuracy value
Spatial Analysis and Machine Learning Integration for Nutritional Status Mapping Using ANN and Random Forest Models Anggraini, Desi Anis; Kurniawan, Fachrul; Nugroho, Fresy; Koeshardianto, Meidya; Iqbal Bachtiar, Mohammad
Knowledge Engineering and Data Science
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Nutritional problems among children under five remain a major public health challenge. This research seeks to create a spatially oriented system for evaluating and mapping nutritional status utilizing Artificial Neural Network (ANN) and Random Forest (RF) algorithms. Data obtained from the Sumenep District Health Office included age, weight, height, and gender variables. Both models were trained using a 70:30 data ratio and evaluated with accuracy, precision, recall, and F1-score metrics. The ANN model achieved an accuracy of 95.8%, while the RF model reached 97.7%. Classification results were visualized through a Geographic Information System (GIS) to illustrate spatial distribution and identify high-risk zones. The integration of machine learning and spatial analysis proved effective in enhancing classification accuracy, improving data interpretation, and supporting data-driven nutritional policy and regional health decision-making.
Co-Authors ., Rahmadwati Aang Subiyakto Abd. Rahman Ahlan Ady Wicaksono Agung Teguh Wibowo Almais Aji Prasetya Wibawa Ajib Hanani Albas, Juan Alfachruddin, M. Nabil Fahd Amani, Holidiyatul Andri Pranolo Anggraini, Desi Anis Anusua Ghosh, Anusua Artanti, Vera Ashar, Muhammad Asrul Sani Atmaja, I Made Ari Dwi Suta Az, Suharyono Azmi, Agus N Balogun, Naeem A Binti Umayah, Binti Camargo, Jair Cengiz, Korhan Dwi Yuniarto Endang Setyati Esther Irawati Setiawan Fachry Khusaini Fresy Nugroho Gianto Widodo Hadi, Muhammad Aulia Syamsul Hani Nurhayati Hartawan, Muhammad S Hoz, César De La Huda, Muhammad Q Hudiono, Subroto Prasetya I Nyoman Gede Arya Astawa Ikhlayel, Mohammed Ilmi, Rifqy Rosdiyah Iqbal Bachtiar, Mohammad Jihad Hammad, Jihad Joan Santoso Kadir, Nurchaerani Karisma, Alfiana Intan Koeshardianto, Meidya Kurubacak, Gulsun Laili, Ufil Hidayatul Leonel Hernandez, Leonel Lokapitasari Belluano, Poetri Lestari M. Faisal Mochammad Hariadi Mubdik, M. Ahsanul Muhammad Faisal Muji, Muji Nashrul Hakiem Nia, Nur Jazilah Purnomo Budi Santoso Purwanto, Christian Nathaniel Qurrohman, Taufiq Ridho, Faiz Mohammad Ririen Kusumawati Siti Helmyati Sri Harini Sugiharto , Tomy Ivan Sunu Jatmika, Sunu Supeno Mardi Susiki Nugroho, Supeno Mardi Supriyono Supriyono Suryatno, Agung Suseno, Hendra B Suyono Suyono Suyono Syaad Patmanthara Triyo Supriyatno Viva Arifin Voliansky, Roman W, Delliana Eka Wahyu Setiawan Yunifa Miftachul Arif Zainal Abidin